Unsupervised extraction of facts

ABSTRACT

A system and method for extracting facts from documents. A fact is extracted from a first document. The attribute and value of the fact extracted from the first document are used as a seed attribute-value pair. A second document containing the seed attribute-value pair is analyzed to determine a contextual pattern used in the second document. The contextual pattern is used to extract other attribute-value pairs from the second document. The extracted attributes and values are stored as facts.

RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.11/394,414, entitled, “Unsupervised Extraction of Facts,” by JonathanBetz and Shubin Zhao, filed on Mar. 31, 2006, which is acontinuation-in-part of U.S. application Ser. No. 11/142,853, entitled,“Learning Facts from Semi-Structured Text,” by Shubin Zhao and JonathanT. Betz, filed on May 31, 2005, now U.S. Pat. No. 7,769,579, issued onAug. 3, 2010, all of which are hereby incorporated by reference.

This application is related to the following applications, all of whichare hereby incorporated by reference:

-   -   U.S. application Ser. No. 11/024,784, entitled, “Supplementing        Search Results with Information of Interest”, by Jonathan Betz,        filed on Dec. 30, 2004;    -   U.S. application Ser. No. 11/142,765, entitled, “Identifying the        Unifying Subject of a Set of Facts”, by Jonathan Betz, filed on        May 31, 2005;    -   U.S. application Ser. No. 11/097,588, entitled, “Corroborating        Facts Extracted from Multiple Sources”, by Jonathan Betz, filed        on Mar. 31, 2005;    -   U.S. application Ser. No. 11/366,162, entitled “Generating        Structured Information,” filed Mar. 1, 2006, by Egon Pasztor and        Daniel Egnor, Attorney Docket number 24207-11149;    -   U.S. application Ser. No. 11/357,748, entitled “Support for        Object Search”, filed Feb. 17, 2006, by Alex Kehlenbeck,        Andrew W. Hogue, Attorney Docket No. 24207-10945;    -   U.S. application Ser. No. 11/342,290, entitled “Data Object        Visualization”, filed on Jan. 27, 2006, by Andrew W. Hogue,        David Vespe, Alex Kehlenbeck, Mike Gordon, Jeffrey C. Reynar,        David Alpert;    -   U.S. application Ser. No. 11/342,293, entitled “Data Object        Visualization Using Maps”, filed on Jan. 27, 2006, by Andrew W.        Hogue, David Vespe, Alex Kehlenbeck, Mike Gordon, Jeffrey C.        Reynar, David Alpert;    -   U.S. application Ser. No. 11/356,679, entitled “Query Language”,        filed Feb. 17, 2006, by Andrew W. Hogue, Doug Rohde, Attorney        Docket No. 24207-10948;    -   U.S. application Ser. No. 11/356,837, entitled “Automatic Object        Reference Identification and Linking in a Browseable Fact        Repository”, filed Feb. 17, 2006, by Andrew W. Hogue, Attorney        Docket No. 24207-10961;    -   U.S. application Ser. No. 11/356,851, entitled “Browseable Fact        Repository”, filed Feb. 17, 2006, by Andrew W. Hogue,        Jonathan T. Betz, Attorney Docket No. 24207-10949;    -   U.S. application Ser. No. 11/356,842, entitled “ID Persistence        Through Normalization”, filed Feb. 17, 2006, by Jonathan T.        Betz, Andrew W. Hogue, Attorney Docket No. 24207-10950;    -   U.S. application Ser. No. 11/356,728, entitled “Annotation        Framework”, filed Feb. 17, 2006, by Tom Richford, Jonathan T.        Betz, Attorney Docket No. 24207-10951;    -   U.S. application Ser. No. 11/341,069, entitled “Object        Categorization for Information Extraction”, filed on Jan. 27,        2006, by Jonathan T. Betz, Attorney Docket No. 24207-10952;    -   U.S. application Ser. No. 11/356,838, entitled “Modular        Architecture for Entity Normalization”, filed Feb. 17, 2006, by        Jonathan T. Betz, Farhan Shamsi, Attorney Docket No.        24207-10953;    -   U.S. application Ser. No. 11/356,765, entitled “Attribute        Entropy as a Signal in Object Normalization”, filed Feb. 17,        2006, by Jonathan T. Betz, Vivek Menezes, Attorney Docket No.        24207-10954;    -   U.S. application Ser. No. 11/341,907, entitled “Designating Data        Objects for Analysis”, filed on Jan. 27, 2006, by Andrew W.        Hogue, David Vespe, Alex Kehlenbeck, Mike Gordon, Jeffrey C.        Reynar, David Alpert;    -   U.S. application Ser. No. 11/342,277, entitled “Data Object        Visualization Using Graphs”, filed on Jan. 27, 2006, by        Andrew W. Hogue, David Vespe, Alex Kehlenbeck, Mike Gordon,        Jeffrey C. Reynar, David Alpert;    -   U.S. application Ser. No. 11/394,508, entitled “Entity        Normalization Via Name Normalization”, filed on Mar. 31, 2006,        by Jonathan T. Betz, Attorney Docket No. 24207-11047;    -   U.S. application Ser. No. 11/394,610, entitled “Determining        Document Subject by Using Title and Anchor Text of Related        Documents”, filed on Mar. 31, 2006, by Shubin Zhao, Attorney        Docket No. 24207-11049;    -   U.S. application Ser. No. 11/394,552, entitled “Anchor Text        Summarization for Corroboration”, filed on Mar. 31, 2006, by        Jonathan T. Betz and Shubin Zhao, Attorney Docket No.        24207-11046;    -   U.S. application Ser. No. 11/399,857, entitled “Mechanism for        Inferring Facts from a Fact Repository”, filed on Mar. 31, 2006,        by Andrew Hogue and Jonathan Betz, Attorney Docket No.        24207-11048.

TECHNICAL FIELD

The disclosed embodiments relate generally to fact databases. Moreparticularly, the disclosed embodiments relate to extracting facts fromdocuments.

BACKGROUND

The internet provides access to a wealth of information. Documentscreated by authors all over the world are freely available for reading,indexing, and extraction of information. This incredible diversity offact and opinion that make the internet the ultimate information source.

However, this same diversity of information creates a considerablechallenge when extracting information. Information may be presented in avariety of formats, languages, and layouts. A human user may (or maynot) be able to decipher individual documents to gather the informationcontained therein, but these differences may confuse or mislead anautomated extraction system, resulting in information of little or novalue. Extracting information from documents of various formats poses aformidable challenge to efforts to create an automated extractionsystem.

SUMMARY

A system and method for extracting facts from documents. A fact isextracted from a first document. The attribute and value of the factextracted from the first document is used as a seed attribute-valuepair. A second document containing the seed attribute-value pair isanalyzed to determine a contextual pattern used in the second document.The contextual pattern is used to extract other attribute-value pairsfrom the second document. The extracted attributes and values are storedas facts.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a network, in accordance with a preferred embodiment of theinvention.

FIGS. 2( a)-2(d) are block diagrams illustrating a data structure forfacts within a repository of FIG. 1 in accordance with preferredembodiments of the invention.

FIG. 2( e) is a block diagram illustrating an alternate data structurefor facts and objects in accordance with preferred embodiments of theinvention.

FIG. 3( a) is a block diagram illustrating the extraction of facts froma plurality of documents, according to one embodiment of the presentinvention.

FIG. 3( b) is a block diagram illustrating the extraction of facts froma plurality of documents to produce an object, according to oneembodiment of the present invention.

FIG. 4 is an example of a document which can be processed usingpredefined patterns, according to one embodiment of the presentinvention.

FIG. 5 is an example of a document which can be processed usingcontextual patterns, according to one embodiment of the presentinvention.

FIG. 6 is a flow chart illustrating a method for extracting facts,according to one embodiment of the present invention.

DESCRIPTION OF EMBODIMENTS

Embodiments of the present invention are now described with reference tothe figures where like reference numbers indicate identical orfunctionally similar elements.

FIG. 1 shows a system architecture 100 adapted to support one embodimentof the invention. FIG. 1 shows components used to add facts into, andretrieve facts from a repository 115. The system architecture 100includes a network 104, through which any number of document hosts 102communicate with a data processing system 106, along with any number ofobject requesters 152, 154.

Document hosts 102 store documents and provide access to documents. Adocument is comprised of any machine-readable data including anycombination of text, graphics, multimedia content, etc. A document maybe encoded in a markup language, such as Hypertext Markup Language(HTML), i.e., a web page, in a interpreted language (e.g., JavaScript)or in any other computer readable or executable format. A document caninclude one or more hyperlinks to other documents. A typical documentwill include one or more facts within its content. A document stored ina document host 102 may be located and/or identified by a UniformResource Locator (URL), or Web address, or any other appropriate form ofidentification and/or location. A document host 102 is implemented by acomputer system, and typically includes a server adapted to communicateover the network 104 via networking protocols (e.g., TCP/IP), as well asapplication and presentation protocols (e.g., HTTP, HTML, SOAP, D-HTML,Java). The documents stored by a host 102 are typically held in a filedirectory, a database, or other data repository. A host 102 can beimplemented in any computing device (e.g., from a PDA or personalcomputer, a workstation, mini-computer, or mainframe, to a cluster orgrid of computers), as well as in any processor architecture oroperating system.

FIG. 1 shows components used to manage facts in a fact repository 115.Data processing system 106 includes one or more importers 108, one ormore janitors 110, a build engine 112, a service engine 114, and a factrepository 115 (also called simply a “repository”). Each of theforegoing are implemented, in one embodiment, as software modules (orprograms) executed by processor 116. Importers 108 operate to processdocuments received from the document hosts, read the data content ofdocuments, and extract facts (as operationally and programmaticallydefined within the data processing system 106) from such documents. Theimporters 108 also determine the subject or subjects with which thefacts are associated, and extract such facts into individual items ofdata, for storage in the fact repository 115. In one embodiment, thereare different types of importers 108 for different types of documents,for example, dependent on the format or document type.

Janitors 110 operate to process facts extracted by importer 108. Thisprocessing can include but is not limited to, data cleansing, objectmerging, and fact induction. In one embodiment, there are a number ofdifferent janitors 110 that perform different types of data managementoperations on the facts. For example, one janitor 110 may traverse someset of facts in the repository 115 to find duplicate facts (that is,facts that convey the same factual information) and merge them. Anotherjanitor 110 may also normalize facts into standard formats. Anotherjanitor 110 may also remove unwanted facts from repository 115, such asfacts related to pornographic content. Other types of janitors 110 maybe implemented, depending on the types of data management functionsdesired, such as translation, compression, spelling or grammarcorrection, and the like.

Various janitors 110 act on facts to normalize attribute names, andvalues and delete duplicate and near-duplicate facts so an object doesnot have redundant information. For example, we might find on one pagethat Britney Spears' birthday is “Dec. 2, 1981” while on another pagethat her date of birth is “Dec. 2, 1981.” Birthday and Date of Birthmight both be rewritten as Birthdate by one janitor and then anotherjanitor might notice that Dec. 2, 1981 and Dec. 2, 1981 are differentforms of the same date. It would choose the preferred form, remove theother fact and combine the source lists for the two facts. As a resultwhen you look at the source pages for this fact, on some you'll find anexact match of the fact and on others text that is considered to besynonymous with the fact.

Build engine 112 builds and manages the repository 115. Service engine114 is an interface for querying the repository 115. Service engine114's main function is to process queries, score matching objects, andreturn them to the caller but it is also used by janitor 110.

Repository 115 stores factual information extracted from a plurality ofdocuments that are located on document hosts 102. A document from whicha particular fact may be extracted is a source document (or “source”) ofthat particular fact. In other words, a source of a fact includes thatfact (or a synonymous fact) within its contents.

Repository 115 contains one or more facts. In one embodiment, each factis associated with exactly one object. One implementation for thisassociation includes in each fact an object ID that uniquely identifiesthe object of the association. In this manner, any number of facts maybe associated with an individual object, by including the object ID forthat object in the facts. In one embodiment, objects themselves are notphysically stored in the repository 115, but rather are defined by theset or group of facts with the same associated object ID, as describedbelow. Further details about facts in repository 115 are describedbelow, in relation to FIGS. 2( a)-2(d).

It should be appreciated that in practice at least some of thecomponents of the data processing system 106 will be distributed overmultiple computers, communicating over a network. For example,repository 115 may be deployed over multiple servers. As anotherexample, the janitors 110 may be located on any number of differentcomputers. For convenience of explanation, however, the components ofthe data processing system 106 are discussed as though they wereimplemented on a single computer.

In another embodiment, some or all of document hosts 102 are located ondata processing system 106 instead of being coupled to data processingsystem 106 by a network. For example, importer 108 may import facts froma database that is a part of or associated with data processing system106.

FIG. 1 also includes components to access repository 115 on behalf ofone or more object requesters 152, 154. Object requesters are entitiesthat request objects from repository 115. Object requesters 152, 154 maybe understood as clients of the system 106, and can be implemented inany computer device or architecture. As shown in FIG. 1, a first objectrequester 152 is located remotely from system 106, while a second objectrequester 154 is located in data processing system 106. For example, ina computer system hosting a blog, the blog may include a reference to anobject whose facts are in repository 115. An object requester 152, suchas a browser displaying the blog will access data processing system 106so that the information of the facts associated with the object can bedisplayed as part of the blog web page. As a second example, janitor 110or other entity considered to be part of data processing system 106 canfunction as object requester 154, requesting the facts of objects fromrepository 115.

FIG. 1 shows that data processing system 106 includes a memory 107 andone or more processors 116. Memory 107 includes importers 108, janitors110, build engine 112, service engine 114, and requester 154, each ofwhich are preferably implemented as instructions stored in memory 107and executable by processor 116. Memory 107 also includes repository115. Repository 115 can be stored in a memory of one or more computersystems or in a type of memory such as a disk. FIG. 1 also includes acomputer readable medium 118 containing, for example, at least one ofimporters 108, janitors 110, build engine 112, service engine 114,requester 154, and at least some portions of repository 115. FIG. 1 alsoincludes one or more input/output devices 120 that allow data to beinput and output to and from data processing system 106. It will beunderstood that data processing system 106 preferably also includesstandard software components such as operating systems and the like andfurther preferably includes standard hardware components not shown inthe figure for clarity of example.

FIG. 2( a) shows an example format of a data structure for facts withinrepository 115, according to some embodiments of the invention. Asdescribed above, the repository 115 includes facts 204. Each fact 204includes a unique identifier for that fact, such as a fact ID 210. Eachfact 204 includes at least an attribute 212 and a value 214. Forexample, a fact associated with an object representing George Washingtonmay include an attribute of “date of birth” and a value of “Feb. 22,1732.” In one embodiment, all facts are stored as alphanumericcharacters since they are extracted from web pages. In anotherembodiment, facts also can store binary data values. Other embodiments,however, may store fact values as mixed types, or in encoded formats.

As described above, each fact is associated with an object ID 209 thatidentifies the object that the fact describes. Thus, each fact that isassociated with a same entity (such as George Washington), will have thesame object ID 209. In one embodiment, objects are not stored asseparate data entities in memory. In this embodiment, the factsassociated with an object contain the same object ID, but no physicalobject exists. In another embodiment, objects are stored as dataentities in memory, and include references (for example, pointers orIDs) to the facts associated with the object. The logical data structureof a fact can take various forms; in general, a fact is represented by atuple that includes a fact ID, an attribute, a value, and an object ID.The storage implementation of a fact can be in any underlying physicaldata structure.

FIG. 2( b) shows an example of facts having respective fact IDs of 10,20, and 30 in repository 115. Facts 10 and 20 are associated with anobject identified by object ID “1.” Fact 10 has an attribute of “Name”and a value of “China.” Fact 20 has an attribute of “Category” and avalue of “Country.” Thus, the object identified by object ID “1” has aname fact 205 with a value of “China” and a category fact 206 with avalue of “Country.” Fact 30 208 has an attribute of “Property” and avalue of” “Bill Clinton was the 42nd President of the United States from1993 to 2001.” Thus, the object identified by object ID “2” has aproperty fact with a fact ID of 30 and a value of “Bill Clinton was the42nd President of the United States from 1993 to 2001.” In theillustrated embodiment, each fact has one attribute and one value. Thenumber of facts associated with an object is not limited; thus whileonly two facts are shown for the “China” object, in practice there maybe dozens, even hundreds of facts associated with a given object. Also,the value fields of a fact need not be limited in size or content. Forexample, a fact about the economy of “China” with an attribute of“Economy” would have a value including several paragraphs of text,numbers, perhaps even tables of figures. This content can be formatted,for example, in a markup language. For example, a fact having anattribute “original html” might have a value of the original html texttaken from the source web page.

Also, while the illustration of FIG. 2( b) shows the explicit coding ofobject ID, fact ID, attribute, and value, in practice the content of thefact can be implicitly coded as well (e.g., the first field being theobject ID, the second field being the fact ID, the third field being theattribute, and the fourth field being the value). Other fields includebut are not limited to: the language used to state the fact (English,etc.), how important the fact is, the source of the fact, a confidencevalue for the fact, and so on.

FIG. 2( c) shows an example object reference table 210 that is used insome embodiments. Not all embodiments include an object reference table.The object reference table 210 functions to efficiently maintain theassociations between object IDs and fact IDs. In the absence of anobject reference table 210, it is also possible to find all facts for agiven object ID by querying the repository to find all facts with aparticular object ID. While FIGS. 2( b) and 2(c) illustrate the objectreference table 210 with explicit coding of object and fact IDs, thetable also may contain just the ID values themselves in column orpair-wise arrangements.

FIG. 2( d) shows an example of a data structure for facts withinrepository 115, according to some embodiments of the invention showingan extended format of facts. In this example, the fields include anobject reference link 216 to another object. The object reference link216 can be an object ID of another object in the repository 115, or areference to the location (e.g., table row) for the object in the objectreference table 210. The object reference link 216 allows facts to haveas values other objects. For example, for an object “United States,”there may be a fact with the attribute of “president” and the value of“George W. Bush,” with “George W. Bush” being an object having its ownfacts in repository 115. In some embodiments, the value field 214 storesthe name of the linked object and the link 216 stores the objectidentifier of the linked object. Thus, this “president” fact wouldinclude the value 214 of “George W. Bush”, and object reference link 216that contains the object ID for the for “George W. Bush” object. In someother embodiments, facts 204 do not include a link field 216 because thevalue 214 of a fact 204 may store a link to another object.

Each fact 204 also may include one or more metrics 218. A metricprovides an indication of the some quality of the fact. In someembodiments, the metrics include a confidence level and an importancelevel. The confidence level indicates the likelihood that the fact iscorrect. The importance level indicates the relevance of the fact to theobject, compared to other facts for the same object. The importancelevel may optionally be viewed as a measure of how vital a fact is to anunderstanding of the entity or concept represented by the object.

Each fact 204 includes a list of one or more sources 220 that includethe fact and from which the fact was extracted. Each source may beidentified by a Uniform Resource Locator (URL), or Web address, or anyother appropriate form of identification and/or location, such as aunique document identifier.

The facts illustrated in FIG. 2( d) include an agent field 222 thatidentifies the importer 108 that extracted the fact. For example, theimporter 108 may be a specialized importer that extracts facts from aspecific source (e.g., the pages of a particular web site, or family ofweb sites) or type of source (e.g., web pages that present factualinformation in tabular form), or an importer 108 that extracts factsfrom free text in documents throughout the Web, and so forth.

Some embodiments include one or more specialized facts, such as a namefact 207 and a property fact 208. A name fact 207 is a fact that conveysa name for the entity or concept represented by the object ID. A namefact 207 includes an attribute 224 of “name” and a value, which is thename of the object. For example, for an object representing the countrySpain, a name fact would have the value “Spain.” A name fact 207, beinga special instance of a general fact 204, includes the same fields asany other fact 204; it has an attribute, a value, a fact ID, metrics,sources, etc. The attribute 224 of a name fact 207 indicates that thefact is a name fact, and the value is the actual name. The name may be astring of characters. An object ID may have one or more associated namefacts, as many entities or concepts can have more than one name. Forexample, an object ID representing Spain may have associated name factsconveying the country's common name “Spain” and the official name“Kingdom of Spain.” As another example, an object ID representing theU.S. Patent and Trademark Office may have associated name factsconveying the agency's acronyms “PTO” and “USPTO” as well as theofficial name “United States Patent and Trademark Office.” If an objectdoes have more than one associated name fact, one of the name facts maybe designated as a primary name and other name facts may be designatedas secondary names, either implicitly or explicitly.

A property fact 208 is a fact that conveys a statement about the entityor concept represented by the object ID. Property facts are generallyused for summary information about an object. A property fact 208, beinga special instance of a general fact 204, also includes the sameparameters (such as attribute, value, fact ID, etc.) as other facts 204.The attribute field 226 of a property fact 208 indicates that the factis a property fact (e.g., attribute is “property”) and the value is astring of text that conveys the statement of interest. For example, forthe object ID representing Bill Clinton, the value of a property factmay be the text string “Bill Clinton was the 42nd President of theUnited States from 1993 to 2001.” “Some object IDs may have one or moreassociated property facts while other objects may have no associatedproperty facts. It should be appreciated that the data structures shownin FIGS. 2( a)-2(d) and described above are merely exemplary. The datastructure of the repository 115 may take on other forms. Other fieldsmay be included in facts and some of the fields described above may beomitted. Additionally, each object ID may have additional special factsaside from name facts and property facts, such as facts conveying a typeor category (for example, person, place, movie, actor, organization,etc.) for categorizing the entity or concept represented by the objectID. In some embodiments, an object's name(s) and/or properties may berepresented by special records that have a different format than thegeneral facts records 204.

As described previously, a collection of facts is associated with anobject ID of an object. An object may become a null or empty object whenfacts are disassociated from the object. A null object can arise in anumber of different ways. One type of null object is an object that hashad all of its facts (including name facts) removed, leaving no factsassociated with its object ID. Another type of null object is an objectthat has all of its associated facts other than name facts removed,leaving only its name fact(s). Alternatively, the object may be a nullobject only if all of its associated name facts are removed. A nullobject represents an entity or concept for which the data processingsystem 106 has no factual information and, as far as the data processingsystem 106 is concerned, does not exist. In some embodiments, facts of anull object may be left in the repository 115, but have their object IDvalues cleared (or have their importance to a negative value). However,the facts of the null object are treated as if they were removed fromthe repository 115. In some other embodiments, facts of null objects arephysically removed from repository 115.

FIG. 2( e) is a block diagram illustrating an alternate data structure290 for facts and objects in accordance with preferred embodiments ofthe invention. In this data structure, an object 290 contains an objectID 292 and references or points to facts 294. Each fact includes a factID 295, an attribute 297, and a value 299. In this embodiment, an object290 actually exists in memory 107.

FIG. 3( a) is a block diagram illustrating the extraction of facts froma plurality of documents, according to one embodiment of the presentinvention. Document 302 and document 308 are analogous to the documentsdescribed herein with reference to FIG. 1. According to one embodimentof the present invention, the document 302 and the document 308 arestored in a document repository (not shown).

The importer 304 processes the document 302 and extracts facts 306. Theimporter 304 may employ any of a variety of methods for extracting thefacts 306 from the document 302, such as one of those described in“Supplementing Search Results with Information of Interest” or in theother incorporated applications. For the purposes of illustration, asingle document 302 is shown in the figure. In practice, importer 304can process a plurality of documents 302 to extract the facts 306.

According to one embodiment of the present invention, the importer 304identifies a predefined pattern in the document 302 and applies thepredefined pattern to extract attribute-value pairs. The extractedattribute-value pairs are then stored as facts 306. As described in“Supplementing Search Results with Information of Interest”, apredefined pattern defines specific, predetermined sections of thedocument which are expected to contain attributes and values. Forexample, in an HTML document, the presence of a text block such as“<BR>*:*<BR>” (where ‘*’ can be any string) may indicate that thedocument contains an attribute-value pair organized according to thepattern “<BR>(attribute text):(value text)<BR>”. Such a pattern ispredefined in the sense that it is one of a known list of patterns to beidentified and applied for extraction in documents. Of course, not everypredefined pattern will necessarily be found in every document;identifying the patterns contained in a document determines which (ifany) of the predefined patterns may be used for extraction on thatdocument with a reasonable expectation of producing validattribute-value pairs. The extracted attribute-value pairs are stored inthe facts 306.

An attribute-value pair is composed of an attribute and its associatedvalue. An attribute-value pair may be stored as a fact, for example, bystoring the attribute in the attribute field of the fact and the valuein the value field of the fact. Extracting a fact is synonymous withextracting at least an attribute-value pair and storing the attributeand value as a fact.

In the example illustrated, document 302 contains at least someattribute-value pairs organized according to one of the predefinedpatterns recognizable by the importer 304. An example of a documentcontaining attribute-value pairs organized according to one of thepredefined patterns recognizable by the importer 304 is described hereinwith reference to FIG. 4. Applying predefined patterns to documentscontaining attribute-value pairs organized according to those patternsbeneficially extracts valuable information without the need for humansupervision.

However, the document 302 may contain other attribute-value pairsorganized differently, such that applying one of the predefined patternsrecognizable by the importer 304 produces incomplete, inconsistent, orerroneous results. Similarly, a document such as the document 308 maycontain attribute-value pairs organized in a manner different from thoseprescribed by the various predefined patterns. It is possible that, theimporter 304 were applied to the document 308, none of the predefinedpatterns recognizable by the importer 304 would be identified in thedocument 308.

Advantageously, one embodiment of the present invention facilitates theextraction of attribute-value pairs organized according to a pattern notitself recognizable by the importer 304. According to one embodiment ofthe present invention, a janitor 310 receives the facts 306 and thedocument 308. If the document 308 contains the same (or similar)attribute-value pairs as at least some of the facts 306, the facts 306may be used to identify a contextual pattern in the document 308. Acontextual pattern is a pattern that is inferred on the basis of thecontext in which known attribute-value pairs appear in a document. Anexample of a contextual pattern in a document is described herein withreference to FIG. 5. The janitor 310 applies the contextual pattern tothe document 308 to extract additional attribute-value pairs. Theseattribute-value pairs are then stored as the facts 312. Severalexemplary methods for identifying a contextual pattern and using it toextract attribute-value pairs are described in “Learning Facts fromSemi-Structured Text.”

According to one embodiment of the present invention the janitor 310additionally corroborates the facts 306 using a corroborating document(not shown). For example, as a result of improperly applied predefinedpatterns (or the document 302 itself), some of the facts 306 may containerrors, inconsistent information, or other factual anomalies. If theattribute-value pair of the fact 306A cannot be found in anycorroborating document, the janitor 310 may reduce the confidence scoreof the fact 306A. Alternatively, if the attribute-value pair of the fact306A is identified in a corroborating document, the confidence score ofthe fact 306A can be increased, and a reference to the corroboratingdocument can be added to the list of sources for that fact. Severalexemplary methods for corroborating facts can be found in “CorroboratingFacts Extracted from Multiple Sources.”

According to one embodiment of the present invention, a plurality ofdocuments are used to import and corroborate a group of facts. From thisgroup of imported facts, those associated with a common name may beaggregated to form the facts 306. The facts 306 may be normalized,merged and/or corroborated, and their confidence score may be adjustedaccordingly (for example, by the janitor 310, or by another janitor).According to one embodiment of the present invention, only facts 306having a confidence score above a threshold are used for identificationof contextual patterns by the janitor 310. Corroborating factsbeneficially improves the consistency of extracted facts, and can reducethe influence of improperly applied predefined patterns on the qualityof the fact database.

The facts 306 and facts 312 may be associated with a common object. Forexample, the facts 306 may be extracted from the document 302 and storedas an object in an object repository. According to one embodiment of thepresent invention, the facts 306 may be associated with an object name.An exemplary method for associating an object name with an object isdescribed in “Identifying a Unifying Subject of a Set of Facts”.According to one embodiment of the present invention, the object name(or another property associated with the facts 302) are used to retrievethe document 308. Using the object name to retrieve the document 308 isone example of a method for finding a document potentially containingattribute-value pairs common with the document 302. As another example,the corroboration janitor 306 could query a search engine for documentscontaining one of the attribute-value pairs of the facts 306. Othermethods will be apparent to one of skill in the art without departingfrom the scope of the present invention.

According to one embodiment of the present invention, the facts 312 arefurther processed by a janitor (either the janitor 310 or anotherjanitor). For example, the facts 312 can be merged with another set offacts (for example, the facts 306), normalized, corroborated, and/orgiven a confidence score. According to one embodiment of the presentinvention, facts 312 having a confidence score above a threshold areadded to a fact repository.

FIG. 3( b) is a block diagram illustrating the extraction of facts froma plurality of documents to produce an object, according to oneembodiment of the present invention. The documents 313 contain at leastone attribute-value pair in common, although this attribute-value pairmay be organized according to different patterns in the variousdocuments. Document 313A and document 313B may or may not describe acommon subject.

The unsupervised fact extractor 314 identifies in document 313A apredefined pattern and applies that pattern to extract a “seed”attribute-value pair. The unsupervised fact extractor 314 uses the seedattribute-value pair to identify a contextual pattern, in either or bothof the documents 313, and applies the contextual pattern to extractadditional attribute-value pairs. A method used by the unsupervised factextractor 314, according to one embodiment of the present invention, isdescribed herein with reference to FIG. 6. The unsupervised factextractor 314 may be composed of any number of sub-components, forexample, the importer 304 and janitor 310 described herein withreference to FIG. 3( a).

The unsupervised fact extractor 314 organizes the extractedattribute-value pairs into an object 316. The unsupervised factextractor 314 may also employ techniques for normalization,corroboration, confidence rating, and others such as those described inthe applications incorporated by reference above. Other methods forprocessing the extracted facts to produce an object will be apparent toone of skill in the art without departing from the scope of the presentinvention. Furthermore, the unsupervised fact extractor 314 has beenshown as receiving two documents and producing one object for thepurposes of illustration only. In practice, the unsupervised factextractor 314 may operate on any number of documents, to extract aplurality of facts to be organized into any number of objects.

By identifying both predefined and contextual patterns in the documents313, the unsupervised fact extractor 314 is able to build objectscontaining more information than extractors relying on predefinedpatterns alone, and without the need for document-specific humantailoring or intervention.

FIG. 4 is an example of a document containing attribute-value pairsorganized according to a predefined pattern. According to one embodimentof the present invention, document 402 may be analogous to the document302 described herein with reference to FIG. 3. Document 402 includesinformation about Britney Spears organized according to a two columntable 404. According to one embodiment of the present invention, the twocolumn table is a predefined pattern recognizable by the unsupervisedfact extractor 314. The pattern specifies that attributes will be in theleft column and that corresponding values will be in the right column.Thus the unsupervised fact extractor 314 may extract from the document402 the following attribute-value pairs using the predefined pattern:(name; Britney Spears), (profession; actress, singer), (date of birth;Dec. 2, 1981), (place of birth; Kentwood, La.), (sign; Sagittarius),(eye color; brown), and (hair color; brown). These attribute-value pairscan then be stored as facts, associated with an object, used as seedattribute-value pairs, and so on.

FIG. 5 is an example of a document 502 from which a contextual patterncan be identified using a seed fact, according to one embodiment of thepresent invention. Document 502 may be analogous to the document 308described herein with reference to FIG. 3. Document 502 includesinformation about Britney Spears. Document 502 illustrates a list 504organized according to a pattern that for the purposes of illustrationcould be considered whimsical. Attributes are in bold, and valuesassociated with those attributes are listed immediately below initalics. Such a pattern might be intuitive to a human user, but if thatparticular pattern is not recognizable to an extractor as a predefinedpattern, using predefined patterns exclusively could result in theincorrect or failed extraction of the attribute-value pairs.

However, the document 502 has several attribute-value pairs in commonwith the document 402. Specifically, the (name; Britney Spears) and(date of birth; Dec. 2, 1981) pairs are contained in both documents. Theunsupervised fact extractor 314 can use one (or both) of these pairs asa seed attribute-value pair to identify a contextual pattern of otherattribute-value pairs. For example, the (name; Britney Spears) pairmight be contained in a context such as the following:<BR><B>Name</B><BR><I>Britney Spears</I>

Thus, using the information extracted from the document 402, theunsupervised fact extractor 314 might identify in document 502 acontextual pattern for attribute-value pairs organized as:<BR><B>(attribute)</B><BR><I>(value)</I>

The common pair comprised of (date of birth; Dec. 2, 1981) may be usedto confirm this contextual pattern, since this pair might also becontained in a context such as: <BR><B>Date of Birth</B><BR><I>Dec. 2,1981</I>

Once the unsupervised fact extractor 314 has identified a contextualpattern, the unsupervised fact extractor 314 uses the contextual patternto extract additional facts from the document 502. Thus the unsupervisedfact extractor 314 may extract from the document 502 the followingattribute-value pairs using the predefined pattern: (Favorite Food;Chicken Parmesan), (Favorite Movie; Back to the Future), and(Profession; Singer-Songwriter).

For the purposes of illustration, the document 502 shows attribute-valuepairs organized according to a single contextual pattern. Documents maycontain multiple and various contextual patterns, or a mix of predefinedpatterns and contextual patterns. Furthermore, the examples ofpredefined patterns and contextual patterns illustrated herein as beenselected for the purposes of illustration only. In some cases theattribute-value pattern used by document 502 may be recognizable as apredefined pattern, and conversely, in some cases the attribute-valuepattern used by document 402 may not be recognizable as a predefinedpattern. Given the scope and diversity of the internet, however, therewill always be some documents containing attribute-value pairs notorganized by a recognizable predefined pattern, and the ability toidentify contextual patterns beneficially facilitates the extraction ofat least some of these pairs.

FIG. 6 is a flow chart illustrating a method for extracting facts,according to one embodiment of the present invention. According to oneembodiment of the present invention, the method is performed by theunsupervised fact extractor 314.

The method begins with a document 302. The document 302 contains anattribute-value pair organized according to a predefined pattern. Theunsupervised fact extractor 314 extracts 604 an attribute-value pairfrom the document 302, producing a seed attribute-value pair 606.According to one embodiment of the present invention, the unsupervisedfact extractor 314 can extract 604 the attribute and value from thedocument by applying a predefined pattern; other methods for extracting604 the attribute and value will be apparent to one of skill in the artwithout departing from the scope of the present invention. Additionally,the unsupervised fact extractor 314 may store the seed attribute-valuepair 606 in a fact (not shown). According to one embodiment of thepresent invention, the fact in which the seed attribute-pair 606 isstored is associated with an object.

The unsupervised fact extractor 314 retrieves 608 a document 610 thatcontains the seed attribute-value pair 606 organized according to acontextual pattern.

According to one embodiment of the present invention, the unsupervisedfact extractor 314 retrieves 608 the document 610 by searching (forexample, on document hosts or in a document repository) for documentscontaining the attribute and value of the seed attribute-value pair.According to another embodiment of the present invention, the seedattribute-value pair is stored as a fact associated with an object. Thisobject may have a name, and the unsupervised fact extractor 314 mayretrieve 608 a document 610 by searching in a document repository fordocuments containing the object name. Other methods for retrieving 608 adocument 610 will be apparent to one of skill in the art withoutdeparting from the scope of the present invention.

The unsupervised fact extractor 314 identifies 612 a contextual patternassociated with the seed attribute-value pair 606 and uses the patternto extract an attribute-value pair 614 from the document 610. Theattribute-value pair 614 may then be stored as a fact and processed byfurther janitors, importers, and object retrievers as appropriate.According to one embodiment of the present invention, the fact in whichthe attribute-value pair 614 is stored is associated with an object. Thefact containing attribute-value pair 614 may be associated with the sameobject as the fact containing seed attribute-value pair 606, or it maybe associated with a different object.

By extracting attributes and value using both predefined and contextualpatterns, the unsupervised fact extractor 314 is able to collect alarger amount of information into facts than an extractor relying oneither approach alone. Advantageously, information may be extracted intofacts efficiently, accurately, and without need for human supervision.

Additionally, the unsupervised fact extractor 314 may also use thecontextual pattern to extract another attribute-value pair from a thirddocument. According to one embodiment of the present invention, theunsupervised fact extractor 314 determines if the third document issimilar to the document 610, for example, by comparing the domainhosting the document 610 to the domain hosting the third document. Usingthe contextual pattern to extract another attribute-value pair from athird document may be responsive to the determination that the thirddocument is similar to the document 610. Using the contextual pattern toextract another attribute-value pair from a third documentadvantageously facilitates the extracting of attribute-value pairsorganized according to patterns not recognizable as predefined patterns,even from documents not containing a seed attribute-value pair.

While a method for extracting facts has been shown for the purposes ofillustration as extracting a single seed attribute-value pair 606 and asingle attribute-value pair 614, it will be apparent to one of skill inthe art that in practice the unsupervised fact extractor 314 may extract604 a plurality of attribute-value pairs and extract 612 a plurality ofattribute-value pairs 614. When a plurality of attribute-value pairs areextracted 604, any number of that plurality may be used as seedattribute-value pairs 606. According to one embodiment of the presentinvention, extracting 612 additional attribute-value pairs from thedocument 610 is responsive to the number of seed-attribute-value pairs606 contained in the document 610. According to another embodiment ofthe present invention, a first seed attribute-value pair 606 may be usedto identify 612 a contextual pattern and a second seed attribute-valuepair 606 may be used to verify that contextual pattern, for example, bydetermining if the second seed attribute-value pair 606 is organized inthe document 610 according to the contextual pattern. By using aplurality of seed attribute-value pairs 606, the efficiency and accuracyof the unsupervised fact extractor 314 may be improved.

Reference in the specification to “one embodiment” or to “an embodiment”means that a particular feature, structure, or characteristic describedin connection with the embodiments is included in at least oneembodiment of the invention. The appearances of the phrase “in oneembodiment” in various places in the specification are not necessarilyall referring to the same embodiment.

Some portions of the above are presented in terms of algorithms andsymbolic representations of operations on data bits within a computermemory. These algorithmic descriptions and representations are the meansused by those skilled in the data processing arts to most effectivelyconvey the substance of their work to others skilled in the art. Analgorithm is here, and generally, conceived to be a self-consistentsequence of steps (instructions) leading to a desired result. The stepsare those requiring physical manipulations of physical quantities.Usually, though not necessarily, these quantities take the form ofelectrical, magnetic or optical signals capable of being stored,transferred, combined, compared and otherwise manipulated. It isconvenient at times, principally for reasons of common usage, to referto these signals as bits, values, elements, symbols, characters, terms,numbers, or the like. Furthermore, it is also convenient at times, torefer to certain arrangements of steps requiring physical manipulationsof physical quantities as modules or code devices, without loss ofgenerality.

It should be borne in mind, however, that all of these and similar termsare to be associated with the appropriate physical quantities and aremerely convenient labels applied to these quantities. Unlessspecifically stated otherwise as apparent from the following discussion,it is appreciated that throughout the description, discussions utilizingterms such as “processing” or “computing” or “calculating” or“determining” or “displaying” or “determining” or the like, refer to theaction and processes of a computer system, or similar electroniccomputing device, that manipulates and transforms data represented asphysical (electronic) quantities within the computer system memories orregisters or other such information storage, transmission or displaydevices.

Certain aspects of the present invention include process steps andinstructions described herein in the form of an algorithm. It should benoted that the process steps and instructions of the present inventioncan be embodied in software, firmware or hardware, and when embodied insoftware, can be downloaded to reside on and be operated from differentplatforms used by a variety of operating systems.

The present invention also relates to an apparatus for performing theoperations herein. This apparatus may be specially constructed for therequired purposes, or it may comprise a general-purpose computerselectively activated or reconfigured by a computer program stored inthe computer. Such a computer program may be stored in a computerreadable storage medium, such as, but is not limited to, any type ofdisk including floppy disks, optical disks, CD-ROMs, magnetic-opticaldisks, read-only memories (ROMs), random access memories (RAMs), EPROMs,EEPROMs, magnetic or optical cards, application specific integratedcircuits (ASICs), or any type of media suitable for storing electronicinstructions, and each coupled to a computer system bus. Furthermore,the computers referred to in the specification may include a singleprocessor or may be architectures employing multiple processor designsfor increased computing capability.

The algorithms and displays presented herein are not inherently relatedto any particular computer or other apparatus. Various general-purposesystems may also be used with programs in accordance with the teachingsherein, or it may prove convenient to construct more specializedapparatus to perform the required method steps. The required structurefor a variety of these systems will appear from the description below.In addition, the present invention is not described with reference toany particular programming language. It will be appreciated that avariety of programming languages may be used to implement the teachingsof the present invention as described herein, and any references belowto specific languages are provided for disclosure of enablement and bestmode of the present invention.

While the invention has been particularly shown and described withreference to a preferred embodiment and several alternate embodiments,it will be understood by persons skilled in the relevant art thatvarious changes in form and details can be made therein withoutdeparting from the spirit and scope of the invention.

Finally, it should be noted that the language used in the specificationhas been principally selected for readability and instructionalpurposes, and may not have been selected to delineate or circumscribethe inventive subject matter. Accordingly, the disclosure of the presentinvention is intended to be illustrative, but not limiting, of the scopeof the invention, which is set forth in the following claims.

What is claimed is:
 1. A computer-implemented method for extracting facts, the method comprising: at a computer system including one or more processors and memory storing one or more programs, the one or more processors executing the one or more programs to perform the operations of: identifying a first fact having an attribute and a value obtained from a first document; retrieving a second document that contains the attribute and the value of the first fact; identifying in the second document a contextual pattern associated with the attribute and value of the first fact; extracting a second fact from the second document using the contextual pattern; and storing the first fact and the second fact in a fact repository of the computer system.
 2. The method of claim 1, further comprising: extracting a third fact from a third document using the contextual pattern.
 3. The method of claim 2, wherein the second document is hosted on a first domain, and wherein the third document is hosted on the first domain.
 4. The method of claim 1, further comprising: associating the first fact with a first object.
 5. The method of claim 4, wherein the first object is associated with an object name, and wherein retrieving the second document comprises searching a repository of documents for a document containing the object name.
 6. The method of claim 4, further comprising associating the second fact with the first object.
 7. The method of claim 1, further comprising: identifying a first plurality of facts from the first document, each fact having an attribute and a value.
 8. The method of claim 7, wherein identifying in the second document a contextual pattern associated with the attribute and value of the first fact comprises: identifying in the second document a contextual pattern associated with the attributes and the values of a number of the first plurality of facts.
 9. The method of claim 8, wherein said extracting said second fact is responsive to the number of the first plurality of facts having attributes and values associated with the contextual pattern.
 10. The method of claim 7, wherein said first plurality of facts includes a third fact, the method further comprising: determining if the third fact is organized in the second document according to the contextual pattern.
 11. The method of claim 1, wherein said first document is different from said second document.
 12. The method of claim 1, wherein retrieving the second document comprises querying a search engine for a document containing the attribute and the value of the first fact.
 13. A system for extracting facts comprising: one or more processors; and memory storing one or more programs to be executed by the one or more processors; the one or more programs comprising instructions for: identifying a first fact having an attribute and a value obtained from a first document; retrieving a second document that contains the attribute and the value of the first fact; identifying in the second document a contextual pattern associated with the attribute and value of the first fact; extracting a second fact from the second document using the contextual pattern, and storing the first fact and the second fact in a fact repository.
 14. The system of claim 13, further comprising: instructions for extracting a third fact from a third document using the contextual pattern.
 15. The system of claim 13, further comprising: instructions for associating the first fact with a first object.
 16. The system of claim 15, wherein the first object is associated with an object name, and wherein the instructions for retrieving the second document comprise instructions for searching a repository of documents for a document containing the object name.
 17. A non-transitory computer readable storage medium storing one or more programs configured for execution by a computer, the one or more programs comprising instructions for: identifying a first fact having an attribute and a value obtained from a first document; retrieving a second document that contains the attribute and the value of the first fact; identifying in the second document a contextual pattern associated with the attribute and value of the first fact; extracting a second fact from the second document using the contextual pattern; and storing the first fact and the second fact in a fact repository.
 18. The non-transitory computer readable storage medium of claim 17, the computer-readable medium further comprising: program code for extracting a third fact from a third document using the contextual pattern.
 19. The non-transitory computer readable storage medium of claim 17, the computer-readable medium further comprising: program code for associating the first fact with a first object.
 20. The non-transitory computer readable storage medium of claim 19, wherein the first object is associated with an object name, and wherein the program code for retrieving the second document comprises program code for searching a repository of documents for a document containing the object name. 